Acoustic Detections: Updates

analysis progress of the acoustic detections, master thesis Lotte Pohl, 16.2.2023

some information

Two tag serial numbers where only detected once: 1293296 and 1293301, and where thus omitted.
Females had a mean TL of 85.84 cm ($\pm$ 10.26, n = 19), and males had a mean TL of 77.64 cm ($\pm$ 3.75, n = 11).

Last time, we wanted to resolve further how detections might differ between sex and season.
I first divided the year into summer (from 1st of April to 30th of September) and winter (rest). During the winter period, however, there was not a single detection. So I then divided the detections by month.
Detections range from April to October.
Overall, 14 females were detected 10655 times and 4 males were detected 285 times.
I divided the study area into 3 parts: WS1 (the first array in the Westerscheldt), WS2 (the second, more east array in the Westerscheldt) and BPNS.

The circle size corresponds to the number of detections from each station each month. It is relative to the total number of detections.
The red circles are detections of females, the blue circles are detections of males.

The circle size corresponds to the number of individuals detected at a station each month. It is relative to the total number of detected individuals.
The red circles are detections of females, the blue circles are detections of males.

barplots

detections per month
individuals per month

The upper facet corresponds to females (f), the lower facet corresponds to males (m).

abacus plot

All detections, the colour denotes the area and the symbol denotes the sex.

interpretations

We can see that females overall dominate the detections. By tagging 19 females and 11 males, we already integrated sex bias into the dataset. This bias further increased by only 4 males in contrast to 14 females being detected by a receiver.
One male was detected in the WS1 area. It must have swum into the array, because the sharks tagged in the WS (5km north of Cadzand) were all female. Between May and August, males are only detected in the BPNS. In the 4 months that males were detected, it was a different male each time (tag 311 in April, 315 in May, 316 in July and tag 295 in August).
In September, 2 females (tag 299 and 307) are detected in the WS2 area. There are only a couple of detections (38, on 22.09.2019 for tag 299 and 09.09.2019 for tag 307). Both these tags were also detected in the WS1 area in September 2019, tag 299 16 days before and 3 days after the detection in the WS2 area, and tag 307 1 day before and 7 days after. So tag 299 could have stayed in WS2 for 19 days, and tag 302 for 8 days.

residency index (RI)

The overall Residency Index (RI) is calculated by dividing the days of acoustic detections by the days of liberty (i.e. the days between the release and the last detection), for each tag.
The monthly Residency Index is calculated by dividing the days of detections in a month by the days of that month, for each tag.
I further subdivided the monthly RI by grouping the detections per tag and month into the three areas (WS1, WS2 and BPNS) to get a Residency Index per area and month.

The left facet corresponds to females (f), the right facet corresponds to males (m).

further analyses

what kind of statistics could I do on these data? I would like to see if there are significant differences between sex, area and month (could also be grouped into season, i.e. early/mid/late summer).
I am not sure how to proceed, because we have different n for males and females, and overall a very small number of individuals. Do you have a statistical method in mind that could come in handy in this case?
In her master thesis, Jolien did a Correspondance Analysis (CA) on her monthly Residency Indices. What do you think of doing that here as well?
I have wanted to analyse the dataset using the R packages actel https://doi.org/10.1111/2041-210X.13503 and RSP https://doi.org/10.1111/2041-210X.13484 for some weeks now. This would be my next step for the acoustic detection data.

--- title: "Acoustic Detections: Updates" execute: echo: false include: false output: html_document: df_print: paged theme: readable toc_float: true toc: true number_sections: false toc_depth: 2 subtitle: analysis progress of the acoustic detections, master thesis Lotte Pohl, 16.2.2023 editor_options: chunk_output_type: console knitr: opts_chunk: collapse: true format: html: page-layout: article code-tools: true fig-width: 8 fig-height: 5 --- ::: column-body-outset ```{r libraries} #| include: false library(tidyverse) library(lubridate) library(leaflet) library(tibble) library(stringr) library(ggsci) library(pander) library(mregions2) library(fuzzyjoin) library(sf) # library(oce) library(plotly) library("FactoMineR") library("factoextra") ``` ```{r knityes_no} knitting <- T # if the document is not knitting right now, the prefix for path is set to ".", otherwise (for knitting) it's set to ".." # pre <- ifelse(knitting %>% isFALSE(), ".", "..") pre <- "C:/Users/lotte.pohl/Documents/github_repos/ADST_Mustelus_asterias" ``` ```{r load_functions} source(paste0(pre, "/functions/functions_DST_presstemplogs.R")) source(paste0(pre, "/functions.R")) ``` ```{r setup} #| include: false knitr::opts_chunk$set(warning = FALSE, message = FALSE) ``` ```{r geo_info} #| include: false BPNS <- mregions2::gaz_search(3293) %>% mregions2::gaz_geometry() # sandbanks <- mregions2::gaz_search(c(2419, 4675, 2421, 2420)) %>% mregions2::gaz_geometry() # for now, as long as mregions2 does not load in rstudio server # bpns_boundaries <- st_read('./spatial_data/bpns_boundaries/bpns_boundaries.shp', layer = 'bpns_boundaries') sandbanks <- st_read(paste0(pre, '/spatial_data/sea_floor_geomorphology/sea_floor_geomorphology.shp'), layer = 'sea_floor_geomorphology') OWFs <- st_read(paste0(pre, "/spatial_data/OWFs_shapefiles/RD20140320_art8_1_20140328.shp")) ``` ```{r map_base} #| include: false map_base <- create_basic_map(include_regions = T) %>% setView(3.5, 51.5, zoom = 10) %>% #setView(-3, 52.5, zoom = 9) %>% addScaleBar(position = "topleft", options = scaleBarOptions(maxWidth = 250, imperial = F)) %>% # add sandbanks addPolygons(data=sandbanks, fillColor = "#FFF2B3", weight = 0, fillOpacity = 0.5, group = "Sandbanks") %>% #label = ~preferredGazetteerName, labelOptions = labelOptions(noHide = F, textOnly = F, direction = "center", textsize = "12px", sticky = FALSE) # add OWFs addPolygons(data=OWFs, color = "red", opacity = 0.5, fillColor = "red", weight = 1, fillOpacity = 0.2, group = "OWFs") %>% # add BPNS boundary addPolygons(data = BPNS, color = "grey", weight = 1, opacity = 1.0, fillOpacity = 0, label = ~preferredGazetteerName, labelOptions = labelOptions(noHide = F, textOnly = F, direction = "center", textsize = "12px", sticky = FALSE), group = "Regions") %>% leafem::addMouseCoordinates() map_base # base_map %>% save_leaflet(path = "./visualisation_ADST/maps_plots", filename = "basic_map_test", filetype = "pdf") #do with other map ``` ```{r general_info} #| include: false project_code <- "ADST-Shark" scientific_name <- "Mustelus asterias" masterias_recaptured_serials <- c("1293295", "1293304", "1293310", "1293312", "1293319", "1293308", "1293321", "1293322") # bounding box min_lon <- 0.8 max_lon <- 5 max_lat <- 53 min_lat <- 50 ``` ```{r load_RData} masterias_info <- read_csv(paste0(pre, "/RData/masterias_info.csv"), show_col_types = FALSE) masterias_info <- masterias_info %>% remove_double_cols() # masterias_ind_per_station <- read_csv(paste0(pre, "/RData/masterias_ind_per_station.csv"), show_col_types = FALSE) masterias_detections <- read_csv(paste0(pre, "/RData/masterias_detections.csv"), show_col_types = FALSE) # masterias_deployments <- read_csv(paste0(pre, "/RData/masterias_deployments.csv"), show_col_types = FALSE) # detections_per_station <- read_csv(paste0(pre, "/RData/detections_per_station.csv"), show_col_types = FALSE) # detections_per_deployment <- read_csv(paste0(pre, "/RData/detections_per_deployment.csv"), show_col_types = FALSE) # close_stations <- read_csv(paste0(pre, "/RData/close_stations.csv"), show_col_types = FALSE) deployments <- read_csv(paste0(pre, "/RData/deployments.csv"), show_col_types = FALSE) ``` ```{r clean_animal_data} masterias_info <- clean_animal_info(masterias_info, masterias_detections) # make dateyear and time column in masterias_detections masterias_detections <- masterias_detections %>% mutate(tag_serial_number = tag_serial_number %>% as.character(), time = date_time %>% as.Date("%H:%M", tz = "UTC"), month = date_time %>% lubridate::month(), month_name = month.abb[month] %>% factor(levels = c("Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct")),) # # masterias_detections$time <- masterias_detections$date_time %>% as.Date("%H:%M", tz = "UTC") # # masterias_detections$season <- ifelse(month(masterias_detections$date_time) >=4 & month(masterias_detections$date_time) < 11, "summer", "winter") # only summer detections anyway # # include months # masterias_detections$month <- masterias_detections$date_time %>% lubridate::month() # masterias_detections$month_name <- # # # input sex and release_date_time into detections dataframe # masterias_detections$tag_serial_number <- masterias_detections$tag_serial_number %>% as.character() masterias_detections <- masterias_detections %>% left_join(masterias_info %>% select(tag_serial_number, sex, release_dateyear), by = "tag_serial_number") # remove tags with only 1 detection detection_summary <- masterias_detections %>% mutate(count = 1) %>% group_by(tag_serial_number) %>% summarise(n_detect = sum(count)) # input into masterias_info masterias_info <- masterias_info %>% left_join(detection_summary, by = "tag_serial_number") tag_serial_number_omit <- detection_summary %>% filter(n_detect == 1) %>% select(tag_serial_number) masterias_detections_clean <- masterias_detections %>% filter(!tag_serial_number %in% tag_serial_number_omit$tag_serial_number) # remove deployments outside of detection time frame deployments <- deployments %>% filter(recover_date_time <= masterias_detections_clean$date_time %>% max()) %>% filter(deploy_date_time > masterias_detections_clean$date_time %>% min()) summary_masterias_length <- masterias_info %>% group_by(sex) %>% summarise(mean_length = length1 %>% mean(), sd_length = length1 %>% sd()) rm(masterias_detections) ``` ## some information - Two tag serial numbers where only detected once: `r tag_serial_number_omit[1,1]` and `r tag_serial_number_omit[2,1]`, and where thus omitted. - Females had a mean TL of `r summary_masterias_length %>% filter(sex == "f") %>% select(mean_length) %>% round(digits = 2) %>% pull()` cm ($\pm$ `r summary_masterias_length %>% filter(sex == "f") %>% select(sd_length) %>% round(digits = 2) %>% pull()`, n = `r masterias_info %>% filter(sex == "f") %>% nrow()`), and males had a mean TL of `r summary_masterias_length %>% filter(sex == "m") %>% select(mean_length) %>% round(digits = 2) %>% pull()` cm ($\pm$ `r summary_masterias_length %>% filter(sex == "m") %>% select(sd_length) %>% round(digits = 2) %>% pull()`, n = `r masterias_info %>% filter(sex == "m") %>% nrow()`).  - Last time, we wanted to resolve further how detections might differ between sex and season. - I first divided the year into summer (from 1st of April to 30th of September) and winter (rest). During the winter period, however, there was not a single detection. So I then divided the detections by month. - Detections range from April to October. ```{r near_stations} open_stations <- deployments %>% distinct(station_name, deploy_latitude, deploy_longitude) # BPNS-cpower reefballs is not in the open deployments, beither is BPNSlottobuoy close_stations <- open_stations %>% filter(between(deploy_latitude, min_lat, max_lat)) %>% filter(between(deploy_longitude, min_lon, max_lon)) rm(open_stations) ``` ```{r map_releases_stations} map_releases_stations <- map_base %>% setView(3.5, 51.5, zoom = 10) %>% # ADD RELEASED TAGS addCircleMarkers(data=masterias_info, lat = ~release_latitude, lng = ~release_longitude, weight= 0,# increase if black circle wanted color = "black", fillOpacity = 0.5, radius = 6, fillColor = "grey", popup = ~paste0("tag ", str_trunc(tag_serial_number, 3, "left", ellipsis = ""), " (release: ", release_dateyear, "), ", sex), group = "released tags") %>% addCircleMarkers(data = close_stations, lat = ~deploy_latitude, lng = ~deploy_longitude, fillColor = "black", weight = 0, radius = 3, fillOpacity = 0.8, popup = ~station_name, group = "stations") %>% addLayersControl(position = "topright", overlayGroups = c("released tags", "deployments"), options = layersControlOptions(collapsed = FALSE)) # map_releases_stations ``` ```{r areas} # make overview of stations where M. asterias was detected and assign area to is (WS1, WS2, BPNS) WS1_lon_threshold <- 3.38 WS2_lon_threshold <- 3.6 masterias_stations <-masterias_detections_clean %>% group_by(station_name) %>% summarise(deploy_latitude = mean(deploy_latitude), deploy_longitude = mean(deploy_longitude)) %>% mutate(area = ifelse(deploy_longitude < WS1_lon_threshold, "BPNS", ifelse(deploy_longitude < WS2_lon_threshold, "WS1", "WS2"))) # include areas in masterias_detections as well masterias_detections_clean <- masterias_detections_clean %>% left_join(masterias_stations %>% select(station_name, area), by = "station_name") # # areas work # col_fun_rd <- ggsci::pal_ucscgb()(3) # pal_rd <- leaflet::colorFactor(col_fun_rd, domain = masterias_stations$area) # # map_base %>% addCircleMarkers(data = masterias_stations, # lat = ~deploy_latitude, # lng = ~deploy_longitude, # label = ~station_name, # color = ~pal_rd(area)) rm(WS1_lon_threshold, WS2_lon_threshold) ``` ```{r info_per_station_month_sex} # maybe less resolution: spring (april - may), early summer (jun - jul), late summer (aug - sept), autumn (october) detections_station_season_sex <- masterias_detections_clean %>% group_by(station_name, month, sex) %>% count() %>% rename(detections_count = n) %>% arrange(desc(detections_count)) # detections_station_season_sex %>% View() masterias_ind_per_station_season_sex <- masterias_detections_clean %>% distinct(station_name, tag_serial_number, month, sex)%>% group_by(station_name, month, sex) %>% count() %>% rename(individuals_count = n) %>% arrange(desc(individuals_count)) # masterias_ind_per_station_season_sex %>% View() masterias_station_month_sex <- masterias_ind_per_station_season_sex %>% left_join(detections_station_season_sex, by = c("station_name", "sex", "month"), multiple = "all") %>% left_join(masterias_detections_clean %>% group_by(station_name) %>% select(station_name, deploy_latitude, deploy_longitude), by = "station_name", multiple = "all") masterias_station_month_sex <- masterias_ind_per_station_season_sex %>% left_join(detections_station_season_sex, by = c("station_name", "sex", "month"), multiple = "all") %>% left_join(masterias_stations, by = "station_name", multiple = "all") # masterias_station_month_sex %>% View() masterias_station_month_sex <- masterias_station_month_sex %>% mutate(month_name = month.abb[month]) masterias_station_month_sex$month_name <- masterias_station_month_sex$month_name %>% factor(levels = c("Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct")) # summarise detections per sex abs_detections <- masterias_station_month_sex %>% group_by(sex) %>% summarise(abs_detect = sum(detections_count)) abs_individuals <- masterias_detections_clean %>% group_by(sex) %>% summarise(abs_ind = tag_serial_number %>% unique() %>% length()) abs_detect_ind <- abs_detections %>% left_join(abs_individuals, by = "sex") # rm(abs_detections, abs_individuals) # find individuals per area per month ind_area_month <- masterias_detections_clean %>% group_by(sex, area, month) %>% summarise(individuals_count = tag_serial_number %>% unique() %>% length()) ind_area_month <- ind_area_month %>% mutate(month_name = month.abb[month]) ind_area_month$month_name <- ind_area_month$month_name %>% factor(levels = c("Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct")) rm(masterias_ind_per_station_season_sex, detections_station_season_sex) ``` - Overall, `r abs_detect_ind %>% filter(sex == "f") %>% select(abs_ind) %>% pull()` females were detected `r abs_detect_ind %>% filter(sex == "f") %>% select(abs_detect) %>% pull()` times and `r abs_detect_ind %>% filter(sex == "m") %>% select(abs_ind) %>% pull()` males were detected `r abs_detect_ind %>% filter(sex == "m") %>% select(abs_detect) %>% pull()` times. - I divided the study area into 3 parts: WS1 (the first array in the Westerscheldt), WS2 (the second, more east array in the Westerscheldt) and BPNS. ## maps ::: panel-tabset ### detections per month - The circle size corresponds to the number of detections from each station each month. It is relative to the total number of detections. - The red circles are detections of females, the blue circles are detections of males. ```{r map_detections_station_month_sex} #| include: true # color palette col_fun <- ggsci::pal_locuszoom()(masterias_station_month_sex$month %>% unique() %>% length()) pal <- leaflet::colorFactor(col_fun, domain = masterias_station_month_sex$month) map_station_month_sex_detect <- map_releases_stations %>% # APRIL addCircleMarkers(data = masterias_station_month_sex %>% filter(month == 4, sex == "f"), lat = ~deploy_latitude, lng = ~deploy_longitude, weight = 3, radius = ~log(((100 * detections_count) / masterias_detections_clean %>% nrow()) +1) * 15, fillOpacity = 0.7, opacity = 1, color = "red", fillColor = "#D43F3A", label = ~paste0("month: ", month_name, ", station: ", station_name, ", (", individuals_count, " individuals, ", detections_count, " detections), ", sex), group = "April") %>% addCircleMarkers(data = masterias_station_month_sex %>% filter(month == 4, sex == "m"), lat = ~deploy_latitude, lng = ~deploy_longitude, weight = 3, radius = ~log(((100 * detections_count) / masterias_detections_clean %>% nrow()) +1) * 15, fillOpacity = 0.7, opacity = 1, color = "blue", fillColor = "#D43F3A", label = ~paste0("month: ", month_name, ", station: ", station_name, ", (", individuals_count, " individuals, ", detections_count, " detections), ", sex), group = "April") %>% # MAY addCircleMarkers(data = masterias_station_month_sex %>% filter(month == 5, sex == "m"), lat = ~deploy_latitude, lng = ~deploy_longitude, weight = 3, radius = ~log(((100 * detections_count) / masterias_detections_clean %>% nrow()) +1) * 15, fillOpacity = 0.7, opacity = 1, color = "blue", fillColor = "#EEA236", label = ~paste0("month: ", month_name, ", station: ", station_name, ", (", individuals_count, " individuals, ", detections_count, " detections), ", sex), group = "May") %>% addCircleMarkers(data = masterias_station_month_sex %>% filter(month == 5, sex == "f"), lat = ~deploy_latitude, lng = ~deploy_longitude, weight = 3, radius = ~log(((100 * detections_count) / masterias_detections_clean %>% nrow()) +1) * 15, fillOpacity = 0.7, opacity = 1, color = "red", fillColor = "#EEA236", label = ~paste0("month: ", month_name, ", station: ", station_name, ", (", individuals_count, " individuals, ", detections_count, " detections), ", sex), group = "May") %>% # JUNE addCircleMarkers(data = masterias_station_month_sex %>% filter(month == 6, sex == "f"), lat = ~deploy_latitude, lng = ~deploy_longitude, weight = 3, radius = ~log(((100 * detections_count) / masterias_detections_clean %>% nrow()) +1) * 15, fillOpacity = 0.7, opacity = 1, color = "red", fillColor = "#5CB85C", label = ~paste0("month: ", month_name, ", station: ", station_name, ", (", individuals_count, " individuals, ", detections_count, " detections), ", sex), group = "June") %>% addCircleMarkers(data = masterias_station_month_sex %>% filter(month == 6, sex == "m"), lat = ~deploy_latitude, lng = ~deploy_longitude, weight = 3, radius = ~log(((100 * detections_count) / masterias_detections_clean %>% nrow()) +1) * 15, fillOpacity = 0.7, opacity = 1, color = "blue", fillColor = "#5CB85C", label = ~paste0("month: ", month_name, ", station: ", station_name, ", (", individuals_count, " individuals, ", detections_count, " detections), ", sex), group = "June") %>% # JULY addCircleMarkers(data = masterias_station_month_sex %>% filter(month == 7, sex == "f"), lat = ~deploy_latitude, lng = ~deploy_longitude, weight = 3, radius = ~log(((100 * detections_count) / masterias_detections_clean %>% nrow()) +1) * 15, fillOpacity = 0.7, opacity = 1, color = "red", fillColor = "#46B8DA", label = ~paste0("month: ", month_name, ", station: ", station_name, ", (", individuals_count, " individuals, ", detections_count, " detections), ", sex), group = "July") %>% addCircleMarkers(data = masterias_station_month_sex %>% filter(month == 7, sex == "m"), lat = ~deploy_latitude, lng = ~deploy_longitude, weight = 3, radius = ~log(((100 * detections_count) / masterias_detections_clean %>% nrow()) +1) * 15, fillOpacity = 0.7, opacity = 1, color = "blue", fillColor = "#46B8DA", label = ~paste0("month: ", month_name, ", station: ", station_name, ", (", individuals_count, " individuals, ", detections_count, " detections), ", sex), group = "July") %>% # AUGUST addCircleMarkers(data = masterias_station_month_sex %>% filter(month == 8, sex == "m"), lat = ~deploy_latitude, lng = ~deploy_longitude, weight = 3, radius = ~log(((100 * detections_count) / masterias_detections_clean %>% nrow()) +1) * 15, fillOpacity = 0.7, opacity = 1, color = "blue", fillColor = "#357EBD", label = ~paste0("month: ", month_name, ", station: ", station_name, ", (", individuals_count, " individuals, ", detections_count, " detections), ", sex), group = "August") %>% addCircleMarkers(data = masterias_station_month_sex %>% filter(month == 8, sex == "f"), lat = ~deploy_latitude, lng = ~deploy_longitude, weight = 3, radius = ~log(((100 * detections_count) / masterias_detections_clean %>% nrow()) +1) * 15, fillOpacity = 0.7, opacity = 1, color = "red", fillColor = "#357EBD", label = ~paste0("month: ", month_name, ", station: ", station_name, ", (", individuals_count, " individuals, ", detections_count, " detections), ", sex), group = "August") %>% # SEPTEMBER addCircleMarkers(data = masterias_station_month_sex %>% filter(month == 9, sex == "f"), lat = ~deploy_latitude, lng = ~deploy_longitude, weight = 3, radius = ~log(((100 * detections_count) / masterias_detections_clean %>% nrow()) +1) * 15, fillOpacity = 0.7, opacity = 1, color = "red", fillColor = "#9632B8", label = ~paste0("month: ", month_name, ", station: ", station_name, ", (", individuals_count, " individuals, ", detections_count, " detections), ", sex), group = "September") %>% addCircleMarkers(data = masterias_station_month_sex %>% filter(month == 9, sex == "m"), lat = ~deploy_latitude, lng = ~deploy_longitude, weight = 3, radius = ~log(((100 * detections_count) / masterias_detections_clean %>% nrow()) +1) * 15, fillOpacity = 0.7, opacity = 1, color = "blue", fillColor = "#9632B8", label = ~paste0("month: ", month_name, ", station: ", station_name, ", (", individuals_count, " individuals, ", detections_count, " detections), ", sex), group = "September") %>% # OCTOBER addCircleMarkers(data = masterias_station_month_sex %>% filter(month == 10, sex == "f"), lat = ~deploy_latitude, lng = ~deploy_longitude, weight = 3, radius = ~log(((100 * detections_count) / masterias_detections_clean %>% nrow()) +1) * 15, fillOpacity = 0.7, opacity = 1, color = "red", fillColor = "#B8B8B8", label = ~paste0("month: ", month_name, ", station: ", station_name, ", (", individuals_count, " individuals, ", detections_count, " detections), ", sex), group = "October") %>% addCircleMarkers(data = masterias_station_month_sex %>% filter(month == 10, sex == "m"), lat = ~deploy_latitude, lng = ~deploy_longitude, weight = 3, radius = ~log(((100 * detections_count) / masterias_detections_clean %>% nrow()) +1) * 15, fillOpacity = 0.7, opacity = 1, color = "blue", fillColor = "#B8B8B8", label = ~paste0("month: ", month_name, ", station: ", station_name, ", (", individuals_count, " individuals, ", detections_count, " detections), ", sex), group = "October") %>% # SETTINGS addLayersControl(position = "topright", overlayGroups = c("April", "May", "June", "July", "August", "September", "October"), options = layersControlOptions(collapsed = FALSE)) %>% hideGroup(c("May", "June", "July", "August", "September", "October")) map_station_month_sex_detect ``` ### individuals per month - The circle size corresponds to the number of individuals detected at a station each month. It is relative to the total number of detected individuals. - The red circles are detections of females, the blue circles are detections of males. ```{r map_individuals_station_month_sex} #| include: true # color palette col_fun <- ggsci::pal_locuszoom()(masterias_station_month_sex$month %>% unique() %>% length()) pal <- leaflet::colorFactor(col_fun, domain = masterias_station_month_sex$month) map_station_month_sex_ind <- map_releases_stations %>% # APRIL addCircleMarkers(data = masterias_station_month_sex %>% filter(month == 4, sex == "m"), lat = ~deploy_latitude, lng = ~deploy_longitude, weight = 3, radius = ~((100 * individuals_count) / masterias_detections_clean %>% select(tag_serial_number) %>% unique() %>% nrow())/ 1.5 + 2, fillOpacity = 0.7, opacity = 1, color = "blue", fillColor = "#D43F3A", label = ~paste0("month: ", month_name, ", station: ", station_name, ", (", individuals_count, " individuals, ", detections_count, " detections), ", sex), group = "April") %>% addCircleMarkers(data = masterias_station_month_sex %>% filter(month == 4, sex == "f"), lat = ~deploy_latitude, lng = ~deploy_longitude, weight = 3, radius = ~((100 * individuals_count) / masterias_detections_clean %>% select(tag_serial_number) %>% unique() %>% nrow())/ 1.5 , fillOpacity = 0.7, opacity = 1, color = "red", fillColor = "#D43F3A", label = ~paste0("month: ", month_name, ", station: ", station_name, ", (", individuals_count, " individuals, ", detections_count, " detections), ", sex), group = "April") %>% # MAY addCircleMarkers(data = masterias_station_month_sex %>% filter(month == 5, sex == "m"), lat = ~deploy_latitude, lng = ~deploy_longitude, weight = 3, radius = ~((100 * individuals_count) / masterias_detections_clean %>% select(tag_serial_number) %>% unique() %>% nrow())/ 1.5 + 2 , fillOpacity = 0.7, opacity = 1, color = "blue", fillColor = "#EEA236", label = ~paste0("month: ", month_name, ", station: ", station_name, ", (", individuals_count, " individuals, ", detections_count, " detections), ", sex), group = "May") %>% addCircleMarkers(data = masterias_station_month_sex %>% filter(month == 5, sex == "f"), lat = ~deploy_latitude, lng = ~deploy_longitude, weight = 3, radius = ~((100 * individuals_count) / masterias_detections_clean %>% select(tag_serial_number) %>% unique() %>% nrow())/ 1.5 , fillOpacity = 0.7, opacity = 1, color = "red", fillColor = "#EEA236", label = ~paste0("month: ", month_name, ", station: ", station_name, ", (", individuals_count, " individuals, ", detections_count, " detections), ", sex), group = "May") %>% # JUNE addCircleMarkers(data = masterias_station_month_sex %>% filter(month == 6, sex == "f"), lat = ~deploy_latitude, lng = ~deploy_longitude, weight = 3, radius = ~((100 * individuals_count) / masterias_detections_clean %>% select(tag_serial_number) %>% unique() %>% nrow())/ 1.5 , fillOpacity = 0.7, opacity = 1, color = "red", fillColor = "#5CB85C", label = ~paste0("month: ", month_name, ", station: ", station_name, ", (", individuals_count, " individuals, ", detections_count, " detections), ", sex), group = "June") %>% addCircleMarkers(data = masterias_station_month_sex %>% filter(month == 6, sex == "m"), lat = ~deploy_latitude, lng = ~deploy_longitude, weight = 3, radius = ~((100 * individuals_count) / masterias_detections_clean %>% select(tag_serial_number) %>% unique() %>% nrow())/ 1.5 + 2 , fillOpacity = 0.7, opacity = 1, color = "blue", fillColor = "#5CB85C", label = ~paste0("month: ", month_name, ", station: ", station_name, ", (", individuals_count, " individuals, ", detections_count, " detections), ", sex), group = "June") %>% # JULY addCircleMarkers(data = masterias_station_month_sex %>% filter(month == 7, sex == "f"), lat = ~deploy_latitude, lng = ~deploy_longitude, weight = 3, radius = ~((100 * individuals_count) / masterias_detections_clean %>% select(tag_serial_number) %>% unique() %>% nrow())/ 1.5 , fillOpacity = 0.7, opacity = 1, color = "red", fillColor = "#46B8DA", label = ~paste0("month: ", month_name, ", station: ", station_name, ", (", individuals_count, " individuals, ", detections_count, " detections), ", sex), group = "July") %>% addCircleMarkers(data = masterias_station_month_sex %>% filter(month == 7, sex == "m"), lat = ~deploy_latitude, lng = ~deploy_longitude, weight = 3, radius = ~((100 * individuals_count) / masterias_detections_clean %>% select(tag_serial_number) %>% unique() %>% nrow())/ 1.5 + 2 , fillOpacity = 0.7, opacity = 1, color = "blue", fillColor = "#46B8DA", label = ~paste0("month: ", month_name, ", station: ", station_name, ", (", individuals_count, " individuals, ", detections_count, " detections), ", sex), group = "July") %>% # AUGUST addCircleMarkers(data = masterias_station_month_sex %>% filter(month == 8, sex == "m"), lat = ~deploy_latitude, lng = ~deploy_longitude, weight = 3, radius = ~((100 * individuals_count) / masterias_detections_clean %>% select(tag_serial_number) %>% unique() %>% nrow())/ 1.5 + 2 , fillOpacity = 0.7, opacity = 1, color = "blue", fillColor = "#357EBD", label = ~paste0("month: ", month_name, ", station: ", station_name, ", (", individuals_count, " individuals, ", detections_count, " detections), ", sex), group = "August") %>% addCircleMarkers(data = masterias_station_month_sex %>% filter(month == 8, sex == "f"), lat = ~deploy_latitude, lng = ~deploy_longitude, weight = 3, radius = ~((100 * individuals_count) / masterias_detections_clean %>% select(tag_serial_number) %>% unique() %>% nrow())/ 1.5 , fillOpacity = 0.7, opacity = 1, color = "red", fillColor = "#357EBD", label = ~paste0("month: ", month_name, ", station: ", station_name, ", (", individuals_count, " individuals, ", detections_count, " detections), ", sex), group = "August") %>% # SEPTEMBER addCircleMarkers(data = masterias_station_month_sex %>% filter(month == 9, sex == "f"), lat = ~deploy_latitude, lng = ~deploy_longitude, weight = 3, radius = ~((100 * individuals_count) / masterias_detections_clean %>% select(tag_serial_number) %>% unique() %>% nrow())/ 1.5 , fillOpacity = 0.7, opacity = 1, color = "red", fillColor = "#9632B8", label = ~paste0("month: ", month_name, ", station: ", station_name, ", (", individuals_count, " individuals, ", detections_count, " detections), ", sex), group = "September") %>% addCircleMarkers(data = masterias_station_month_sex %>% filter(month == 9, sex == "m"), lat = ~deploy_latitude, lng = ~deploy_longitude, weight = 3, radius = ~((100 * individuals_count) / masterias_detections_clean %>% select(tag_serial_number) %>% unique() %>% nrow())/ 1.5 + 2 , fillOpacity = 0.7, opacity = 1, color = "blue", fillColor = "#9632B8", label = ~paste0("month: ", month_name, ", station: ", station_name, ", (", individuals_count, " individuals, ", detections_count, " detections), ", sex), group = "September") %>% # OCTOBER addCircleMarkers(data = masterias_station_month_sex %>% filter(month == 10, sex == "f"), lat = ~deploy_latitude, lng = ~deploy_longitude, weight = 3, radius = ~((100 * individuals_count) / masterias_detections_clean %>% select(tag_serial_number) %>% unique() %>% nrow())/ 1.5 , fillOpacity = 0.7, opacity = 1, color = "red", fillColor = "#B8B8B8", label = ~paste0("month: ", month_name, ", station: ", station_name, ", (", individuals_count, " individuals, ", detections_count, " detections), ", sex), group = "October") %>% addCircleMarkers(data = masterias_station_month_sex %>% filter(month == 10, sex == "m"), lat = ~deploy_latitude, lng = ~deploy_longitude, weight = 3, radius = ~((100 * individuals_count) / masterias_detections_clean %>% select(tag_serial_number) %>% unique() %>% nrow())/ 1.5 + 2 , fillOpacity = 0.7, opacity = 1, color = "blue", fillColor = "#B8B8B8", label = ~paste0("month: ", month_name, ", station: ", station_name, ", (", individuals_count, " individuals, ", detections_count, " detections), ", sex), group = "October") %>% # SETTINGS addLayersControl(position = "topright", overlayGroups = c("April", "May", "June", "July", "August", "September", "October"), options = layersControlOptions(collapsed = FALSE)) %>% hideGroup(c("May", "June", "July", "August", "September", "October")) map_station_month_sex_ind ``` ::: ## barplots ::: panel-tabset ### detections per month ```{r plot_barplot} #| include: true ggplot(masterias_station_month_sex, aes(x = month, y = detections_count, fill = area)) + geom_bar(position = "stack", stat = "identity") + facet_grid(sex ~ ., scales = "free_y", space = "free_y") + theme_minimal() + ggsci::scale_fill_jco() + labs(x = "Month", y = "Detections", fill = "Area") ``` *The upper facet corresponds to females (f), the lower facet corresponds to males (m)*. ### individuals per month ```{r plot_barplot2} #| include: true # need to find individuals per area per month ggplot(ind_area_month, aes(x = month_name, y = individuals_count, fill = area)) + geom_bar(position = "stack", stat = "identity") + facet_grid(sex ~ ., scales = "free_y", space = "free_y") + theme_minimal() + # scale_y_discrete() + scale_y_continuous(n.breaks= 2) + ggsci::scale_fill_jco() + labs(x = "Month", y = "Individuals", fill = "Area") ``` *The upper facet corresponds to females (f), the lower facet corresponds to males (m)*. ::: ## abacus plot All detections, the colour denotes the area and the symbol denotes the sex. ```{r plot_abacus} #| include: true ggplot(masterias_detections_clean) + geom_point(mapping = aes(x = date_time, y = tag_serial_number, color = area, pch = sex), size = 5) + ggsci::scale_color_jco() + labs(x = "Date", y = "Tag serial number", color = "Area", pch = "Sex") + theme_minimal() ``` ## interpretations - We can see that females overall dominate the detections. By tagging 19 females and 11 males, we already integrated sex bias into the dataset. This bias further increased by only 4 males in contrast to 14 females being detected by a receiver. - One male was detected in the WS1 area. It must have swum into the array, because the sharks tagged in the WS (5km north of Cadzand) were all female. Between May and August, males are only detected in the BPNS. In the 4 months that males were detected, it was a different male each time (tag 311 in April, 315 in May, 316 in July and tag 295 in August). ```{r investigation_males} masterias_detections_clean %>% filter(sex == "m") %>% group_by(tag_serial_number, month, area) %>% summarise(individuals_count = tag_serial_number %>% unique() %>% length()) ``` - In September, 2 females (tag 299 and 307) are detected in the WS2 area. There are only a couple of detections (`r masterias_station_month_sex %>% ungroup() %>% filter(area == "WS2") %>% select(detections_count) %>% sum()`, on 22.09.2019 for tag 299 and 09.09.2019 for tag 307). Both these tags were also detected in the WS1 area in September 2019, tag 299 16 days before and 3 days after the detection in the WS2 area, and tag 307 1 day before and 7 days after. So tag 299 could have stayed in WS2 for 19 days, and tag 302 for 8 days. ```{r investigation_females_WS2_WS1} masterias_detections_clean %>% filter(area == "WS2") %>% select(station_name, tag_serial_number, sex) %>% View() masterias_detections_clean %>% filter(area == "WS2") %>% select(tag_serial_number, date_time) %>% unique() %>% View() masterias_detections_clean %>% filter(area == "WS1" & month == "9", detection_year == 19, tag_serial_number %in% c("1293299", "1293307")) %>% select(tag_serial_number, date_time) %>% unique() %>% View() ``` ## residency index (RI) - The **overall Residency Index** (RI) is calculated by dividing the days of acoustic detections by the days of liberty (i.e. the days between the release and the last detection), for each tag. - The **monthly Residency Index** is calculated by dividing the days of detections in a month by the days of that month, for each tag. - I further subdivided the monthly RI by grouping the detections per tag and month into the three areas (WS1, WS2 and BPNS) to get a **Residency Index per area and month**. ```{r RI_calc} # overall residency index, detected days / days of liberty masterias_RI_all <- masterias_detections_clean %>% group_by(tag_serial_number, sex) %>% summarise( release = release_dateyear %>% unique(), min_detect = min(detection_dateyear), max_detect = max(detection_dateyear), days_at_liberty = (max_detect - release) %>% as.numeric(), days_detected = detection_dateyear %>% unique() %>% length(), RI_all = days_detected / days_at_liberty) masterias_RI_all %>% View() # monthly RI per station masterias_RI_month <- masterias_detections_clean %>% group_by(tag_serial_number, month_name, sex) %>% summarise(days_detected = detection_dateyear %>% unique() %>% length(), month_days = month %>% lubridate::days_in_month() %>% as.numeric() %>% unique(), RI_month = days_detected / month_days) masterias_RI_month %>% View() masterias_RI_month_summary <- masterias_RI_month %>% group_by(month_name, sex) %>% summarise(mean_RI_month = mean(RI_month), sd_RI_month = sd(RI_month)) masterias_RI_area <- masterias_detections_clean %>% group_by(tag_serial_number, month_name, area, sex) %>% summarise(days_detected = detection_dateyear %>% unique() %>% length(), month_days = month %>% lubridate::days_in_month() %>% as.numeric() %>% unique(), RI_month = days_detected / month_days) masterias_RI_area %>% View() ``` ```{r RI_plot_ideas} # -------- month # point plot ggplot() + geom_point(data = masterias_RI_month, mapping = aes(x = month_name, y = RI_month, pch = sex, color = sex), size = 4) + geom_point(data = masterias_RI_month_summary, mapping = aes(x = month_name, y = mean_RI_month, color = sex), size = 5, pch = 4) + # scale_y_continuous(expand = c(0,0)) + ggsci::scale_color_nejm() + labs(x = "Month", y = "monthly Residency Index") + theme_minimal() # line plot ggplot(data = masterias_RI_month) + geom_point(mapping = aes(x = month_name, y = RI_month), size = 3.5) + # scale_y_continuous(expand = c(0,0)) + # ggsci::scale_color_jco() + theme_minimal() # boxplot ggplot(masterias_RI_month) + geom_boxplot(aes(x = month_name, y = RI_month, fill = sex)) + # facet_wrap(~sex) + ggsci::scale_fill_nejm() + labs(x = "monthly Residency Index") + theme_minimal() #--------------- area # RI_area ggplot(masterias_RI_area) + geom_boxplot(aes(x = month_name, y = RI_month, fill = area)) + facet_wrap(~sex) + ggsci::scale_fill_jco() + labs(y = "monthly Residency Index", x = "Month", fill = "Area") + theme_minimal() ``` ::: panel-tabset ### overall RI ```{r plot_RI_overall} #| include: true ggplot() + geom_col(data = masterias_RI_all, aes(y = RI_all, x = tag_serial_number, fill = sex)) + # scale_color_continuous(c("red", "blue")) + theme_minimal() + ggsci::scale_fill_nejm() + theme(axis.text.x = element_text(angle = 60, hjust = 1)) + labs(y = "overall Residency index", x = "Tag Serial Number", fill = "Sex") ``` ### monthly RI ```{r plot_RI_month} #| include: true ggplot(masterias_RI_month) + geom_boxplot(aes(x = month_name, y = RI_month, fill = sex)) + # facet_wrap(~sex) + ggsci::scale_fill_nejm() + labs(y = "monthly Residency Index", x = "Month") + theme_minimal() ``` ### RI per area and month ```{r plot_RI_area} #| include: true ggplot(masterias_RI_area) + geom_boxplot(aes(x = month_name, y = RI_month, fill = area)) + facet_wrap(~sex) + ggsci::scale_fill_jco() + labs(y = "monthly Residency Index", x = "Month", fill = "Area") + theme_minimal() ``` *The left facet corresponds to females (f), the right facet corresponds to males (m)*. :::                ## further analyses - what kind of statistics could I do on these data? I would like to see if there are significant differences between sex, area and month (could also be grouped into season, i.e. early/mid/late summer). - I am not sure how to proceed, because we have different n for males and females, and overall a very small number of individuals. Do you have a statistical method in mind that could come in handy in this case? - In her master thesis, Jolien did a Correspondance Analysis (CA) on her monthly Residency Indices. What do you think of doing that here as well? - I have wanted to analyse the dataset using the R packages *actel* <https://doi.org/10.1111/2041-210X.13503> and *RSP* <https://doi.org/10.1111/2041-210X.13484> for some weeks now. This would be my next step for the acoustic detection data. :::